We present a method for training CNN-based object class detectors directly
using mean average precision (mAP) as the training loss, in a truly end-to-end
fashion that includes non-maximum suppression (NMS) at training time. This
contrasts with the traditional approach of training a CNN for a window
classification loss, then applying NMS only at test time, when mAP is used as
the evaluation metric in place of classification accuracy. However, mAP
following NMS forms a piecewise-constant structured loss over thousands of
windows, with gradients that do not convey useful information for gradient
descent. Hence, we define new, general gradient-like quantities for piecewise
constant functions, which have wide applicability. We describe how to calculate
these efficiently for mAP following NMS, enabling to train a detector based on
Fast R-CNN directly for mAP. This model achieves equivalent performance to the
standard Fast R-CNN on the PASCAL VOC 2007 and 2012 datasets, while being
conceptually more appealing as the very same model and loss are used at both
training and test time.

该文研究了一种利用均值平均精度作为训练损失的方法来直接训练 CNN 基础物体分类器，并探讨了如何有效计算这些损失的梯度。该方法在训练时同时使用非极大值抑制，使得训练与测试时使用相同的模型和损失函数。在 PASCAL VOC 2007 和 2012 数据集上，该模型达到了与标准 Fast R-CNN 相同的性能水平。

用于平均精度目标类别检测器的端到端训练

End-to-end training of object class detectors for mean average precision

Object class detectors typically apply a window classifier to all the windows
in a large set, either in a sliding window manner or using object proposals. In
this paper, we develop an active search strategy that sequentially chooses the
next window to evaluate based on all the information gathered before. This
results in a substantial reduction in the number of classifier evaluations and
in a more elegant approach in general. Our search strategy is guided by two
forces. First, we exploit context as the statistical relation between the
appearance of a window and its location relative to the object, as observed in
the training set. This enables to jump across distant regions in the image
(e.g. observing a sky region suggests that cars might be far below) and is done
efficiently in a Random Forest framework. Second, we exploit the score of the
classifier to attract the search to promising areas surrounding a highly scored
window, and to keep away from areas near low scored ones. Our search strategy
can be applied on top of any classifier as it treats it as a black-box. In
experiments with R-CNN on the challenging SUN2012 dataset, our method matches
the detection accuracy of evaluating all windows independently, while
evaluating 9x fewer windows.

该论文提出了一种基于激活搜索策略的窗口分类器方法，该方法利用上下文以及分类器的分数来引导搜索，该方法在 SUN2012 数据集上与 R-CNN 的结果相当，但仅评估了 9 倍的窗口。